Key Responsibilities

– Design and implement fraud prevention strategies tailored to the specific risks and challenges of online payment transactions.

– Conduct thorough analyses of transactional data to identify patterns, trends, and anomalies indicative of fraudulent activity.

– Collaborate with data scientists and engineers to develop and deploy machine learning models and algorithms for fraud detection.

– Monitor Objectives and Key Results (OKR) related to fraud detection and prevention, and proactively adjust strategies to optimize effectiveness.

– Work closely with operation team to investigate suspected fraudulent transactions and take appropriate action.

– Generate reports and communicate findings to management, highlighting areas of concern and proposing actionable recommendations for improvement.

– Contribute to the construction and enhancement of our fraud platform, aiming to bolster fraud detection capabilities, while also fortifying the system's scalability and flexibility.

– Stay updated on emerging trends and technologies in fraud detection and prevention, and incorporate best practices into our processes and systems.

Qualifications

– 8+ years’ experience in fraud prevention or risk management.

– Master’s degree in a relevant field such as Computer Science, Data Science, Mathematics, or Statistics.

– Strong analytical skills with the ability to interpret complex data sets and draw actionable insights.

– Experience working in fast-paced and rapidly changing working environment.

– Proficiency in data analysis tools and programming languages such as Python, R, SQL, etc.

– Familiarity with machine learning techniques and algorithms for fraud detection (e.g., logistic regression, decision trees, random forests, neural networks, isolation forests, etc.).

– Excellent communication skills with the ability to effectively collaborate with cross-functional teams.

– Detail-oriented and self-motivated with a strong commitment to quality and continuous improvement.

– Experience with payments (particularly working with credit cards) is preferred